• Data integration for structure & dynamics: Build ingestion/curation pipelines for structural/biophysical data (mmCIF/PDB, EM maps/particles, binding/biophysics, spectroscopy); implement map/volume preprocessing (e.g., resolution filtering, normalization) and alignment to model inputs/outputs.
• Cryo-EM expertise: Operationalize end-to-end flows from raw image stacks/particles to 3D maps and model-ready tensors; interoperate with community formats (e.g., EMDB/EMPIAR, mmCIF) and link to sequences/annotations.
• Signal & information content: Design dataset diagnostics (e.g., mutual-information-like measures, effective sample size, SNR proxies) to quantify what data teach the model; build active-learning loops that maximize learning per euro of data collection time.
• Model-aware data services: Implement scalable, versioned data services and feature stores that feed training/evaluation; design loaders/augmentations optimized for throughput and correctness (GPU-aware).
• Training-at-scale engineering: Own distributed data pipelines and orchestration for large runs on Azure; profile and tune I/O, storage tiers, data locality, and caching; monitor cost, utilization, and failure modes.
• Quality, governance, and reproducibility: Codify schemas/ontologies, metadata contracts, unit/integration tests, and lineage; automate validation and data drift detection; maintain documentation and examples.
• Partner across disciplines: Work closely with ML researchers, structural biologists, and drug designers; translate experimental constraints into robust computational workflows; communicate clearly and proactively.

Do you have a Doctoral degree?, We seek a highly motivated Senior RSDE to join our Biomolecular Emulator (BioEmu) team. The BioEmu project aims to model the dynamics and function of proteins - how they change shape, bind to each other, and bind small molecules. This approach will help us to understand biological function and dysfunction on a structural level and lead to more effective and targeted drug discovery. Our BioEmu-1 model was published in Science (see our blog post for links to our open-source software and other resources and this explainer video)., Required:

• PhD or equivalent experience in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field.
• Strong software engineering in Python (packaging, testing, CI), with systems thinking for data-intensive ML.
• Deep learning experience (PyTorch/JAX/TensorFlow) and solid foundations in linear algebra, probability, and statistics.
• Proven experience designing robust data pipelines for large-scale ML (HPC or cloud).
• Ability to reason about learning signal and to assess information content of real-world scientific datasets.
• Excellent collaboration and communication in interdisciplinary teams.

Preferred:

• Hands-on cryo-EM experience (e.g., map reconstruction, refinement, or pipeline tooling).
• CUDA or C++ for performance-critical components; experience with mixed precision and memory-efficient training.
• Experience integrating experimental data into ML models (e.g., constraints/priors from cryo-EM, binding assays, spectroscopy).
• Familiarity with MD data, structure prediction systems, or protein design work-flows.
• Experience with cost-optimization for data collection and cloud utilization; clear track record of building reliable, maintainable research software at scale.
• Experience with structural biology or molecular biology data/techniques (e.g., cryo-EM, binding assays, spectroscopy, expression, sequencing)